Molecular Design TRIZ Cases
- Ligand-Macromolecule Complex Modeling via Substructure Mapping
A computer-based method maps spatial relationships between query ligand substructures and target macromolecules to generate 3-D structural models.
- Ligand Affinity Prediction via Spatial Interaction Mapping
Computational method predicts ligand modifications by mapping non-bonding atom contacts to protein binding sites.
- Tau Protein Cryptic Pocket Stabilization for Aggregation Inhibition
Computational protocol identifies cryptic binding pockets in tau protein fragments to stabilize specific conformations and prevent aggregation.
- Binding Assay Method Using Saturated Receptor Measurement
A binding assay method utilizes saturated receptor measurement in n-curve analysis to determine Kd and Rt values without completing the full concentration range.
- Core Hopping for Lead Compound Binding Prediction
Computational method partitions lead compounds into core and non-core regions to identify alternative cores.
- Stereoisomer Generation via Fragment Segmentation and Assembly
A method segments organic molecules into standardized fragments to automatically generate all possible stereoisomer combinations.
- Retrosynthesis Neural Network Parallel Training Optimization
Target neural network processes molecular representations via distributed parallel computing to accelerate retrosynthesis route generation.
- Computational Drug Discovery System for Ligand-Receptor Interaction Analysis
A computational drug discovery system performs pose and free energy calculations to determine ligand-receptor interactions.
- Drug Reaction Prediction via Multi-Level Graph Prompt Learning
Multi-level graph prompt learning integrates entity and interaction graphs to resolve the accuracy-complexity trade-off in drug reaction prediction.
- SAFE Representation Generative Chemistry Model
Continuous latent space optimization via variational autoencoders enables gradient-based scaffold decoration for specific protein targets.
- Molecular Graph Inference via Tree Decomposition
A reinforcement learning model infers molecular structures using a tree representation with site information to maintain connection data.
- DNMT1 Transition State Inhibitors for Non-Mutagenic Cancer Therapy
Transition state analogs mimic DNMT1 geometry to inhibit enzyme activity, avoiding mutagenic DNA incorporation found in current cancer therapies.
- In Silico Molecular Docking for Personalized Cancer Therapy
Computational docking predicts drug efficacy against individual mutations, reducing development time and side effects.